Lambda

AI cloud infrastructure

StaffStorageEngineer

$349–465k San Francisco, California, United States FULL TIME
Market Sentiment
HIGH DEMAND

Neural analysis suggests this role is
optimal for Lead candidates.

The Brief

“Staff Storage Engineer at Lambda. Skills: Storage solutions, Infrastructure as Code, AI/ML workload profiles. Lead RFP process. Drive storage solution selection”

Industry & Context.

AI cloud infrastructure
Problems you'll solve

Diagnosing performance and health across hardware and fabrics; Proactive remediation

Eligibility Requirements

Presence in San Francisco or San Jose office location 4 days per week

What They're Looking For.

Must Have

8+ years of experience designing, building, and operating large-scale multi-petabyte storage production environments, Familiarity with one or more storage solutions of the following vendors: Vast, Weka, DDN, NetApp, PureStorage, Dell, IBM, HPE, File, Block, and Object storage types, Storage Network Access Protocols such as NFS, SMB, and POSIX-compliant protocols, NVMEoverFabricStorage Transport Protocols: NVME/TCP, NVME/IB, or NVME/RoCE, Storage performance via RDMA, GPUDirect Storage, parallel file systems, Encryption, storage security, and multi-tenancy strategies, Storage data-reduction, compression, and encryption, Backup and data protection, 5+ years of experience in Infrastructure as Code (e. g. Terraform, Ansible)

Nice to Have

Experience with Kubernetes, including CSI and COSI drivers and CNI’s, Deep understanding of storage performance, understanding of public cloud features (e. g. , SDN, block storage, distributed file systems, identity management), Experience deploying, operating, and maintaining Software Defined Storage, Have implemented either open-source or commercial monitoring solutions of storage and storage-adjacent solutions

What You'll Do.

Drive storage solution selection

Develop understanding of AI/ML workload profiles

Influence future storage architecture

Optimize storage performance

Identify operational improvements

Lead deployment plans

Gather technical requirements

Inform solution design

Delegate engineering tasks

Maintain communication with leadership

How You'll Work.

Team & Collaboration

Partner with leadership during deal formation; Cross-functional deployment plans; Proactive communication with engineering leadership team

Communication Scope

Consistent, proactive communication

Full Job Description

Lambda, The Superintelligence Cloud, is a leader in AI cloud infrastructure serving tens of thousands of customers. Our customers range from AI researchers to enterprises and hyperscalers. Lambda's mission is to make compute as ubiquitous as electricity and give everyone the power of superintelligence. One person, one GPU. If you'd like to build the world's best AI cloud, join us. *Note: This position requires presence in our San Francisco or San Jose office location 4 days per week; Lambda’s designated work from home day is currently Tuesday. Product Engineering at Lambda is responsible for building and scaling our cloud offering. Our scope includes the Lambda website, cloud APIs and systems as well as internal tooling for system deployment, management and maintenance. For distributed AI workloads, GPU compute power is only one factor. High-performance networking and storage are essential for interconnecting these systems and supporting AI training and inference at scale. Lambda’s Infrastructure Engineering team integrates advanced storage, networking, and compute hardware to build high-performance clusters. Our expertise lies at the intersection of: - High-Performance Distributed Storage Solutions: We deploy and maintain the storage systems that provide customer training and inference datasets at the speeds demanded by modern clustered GPUs. - Software Defined Networking: We deploy software defined network overlays that provide multi-tenant security and intelligent routing without compromising performance, using the latest in high-performance networking hardware. - Compute Virtualization: We enable virtualization that allows AI researchers and engineers to focus on AI workloads, not AI infrastructure. - Cluster Integrity: We own the cluster integrity lifecycle: validating deployments, diagnosing performance and health across hardware and fabrics, and providing proactive remediation. About the Role: You will focus on strategy, architecture, and organizational influ

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